Multiplayer fighting videogames have become an
increasingly popular over the last few years, especially with the
introduction of online play, making for a more competitive
experience. Multiplayer fighting games give players the
opportunity to utilize particular strategies and tactics to win,
allowing them to use their own signature style. As a player can
only play against a particular opponent who is actively
participating in the game themselves, they cannot practice
combating the opponent’s style if the opponent is not
participating in the game. This paper presents a novel approach
for an avatar to learn and mimic the style of a player. It does this
by recording and analyzing the data before splitting it up into
two tiers; tactical data and strategic data.. The approach uses a
Naïve Bayes classifier to classify the tactics to particular states,
and a Data Driven Finite State Machine to dictate when certain
tactics are used. Statistics recorded during an experiment
involving the approach are discussed, which indicate that the
architecture of the Artificial Intelligence is fit for purpose, but
does require refinement. Limitations of the architecture are
discussed, including that such an approach may not provide
accurate results when more parameters are considered.